DETAILED ACTION
Application No. 18/858,230 filed on 10/18/2024 has been examined. In this Office Action, claims 1-3, 6-8, 10-11, 14-15, 17-21, 23 and 27-30 are pending.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/17/2025 and 12/02/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or
composition of matter, or any new and useful improvement thereof, may obtain a patent
therefor, subject to the conditions and requirements of this title.
Claims 1-3, 6-8, 10-11, 14-15, 17-21, 23 and 27-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Based upon consideration of all of the relevant factors with respect to the claims as a whole, claims 1-3, 6-8, 10-11, 14-15, 17-21, 23 and 27-30 are determined to be directed to an abstract idea and not significantly more than the abstract idea itself. The rationale for this determination is explained below:
Claims 1, 27:
At Step 1:
The claims are directed to “a method”, and thus directed to a statutory category.
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
The limitation of “transmitting a search query for retrieving search results indicating web resources related to the search query”, as drafted is a process that, under broadest reasonable interpretation, covers mental process.
The limitation of “determining based on context data associated with the search query, a resource locator of an action interface of a web resource associated with at least one search result, wherein the machine- learned action prediction model was trained using a training set of action sequences, a respective training action sequence describing an order in which web resources were accessed”, as drafted is a process that, under broadest reasonable interpretation, covers mental process.
The limitation of “obtaining a training set of action sequences received from a browser application of a first set of client devices”, as drafted is a process that, under broadest reasonable interpretation, covers mental process.
The limitation of “training, by the computing system, a machine-learned action prediction model to predict, for a respective training action sequence, an action based on one or more preceding actions in the respective training action sequence”, as drafted is a process that, under broadest reasonable interpretation, covers mental process.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“browser application, device” which are all a high-level recitation of a generic computer components and represent mere instructions to apply the judicial exception on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application and/or is Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP §2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). Therefore, the limitation does not recite any improvement to the technology.
-“ retrieving search results indicating web resources related to the search query”, is insignificant extra-solution activity as mere data gathering such as ‘obtaining information’. See MPEP 2106.05(g).
-“ generating, by the computing system, a shortcut to the action interface using the resource locator” is insignificant extra-solution activity as mere data gathering such as ‘obtaining information’. See MPEP 2106.05(g).
-“ outputting, by the computing system on a user interface, the at least one search result and the shortcut” is insignificant extra-solution activity as mere data gathering such as ‘obtaining information’. See MPEP 2106.05(g).
-“ outputting, by the computing system, the trained machine-learned action prediction model to update a browser application of a second set of client devices, the browser application configured to use the trained machine-learned model to determine a resource locator of an action interface of a web resource associated with at least one search result presented within the browser” is insignificant extra-solution activity as mere data gathering such as ‘obtaining information’. See MPEP 2106.05(g).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“ retrieving search results indicating web resources related to the search query” is WURC as evidenced by the court cases cited in MPEP 2106.05(d)(II) by at least "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, ... buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" and "iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, ... Of P Techs., 788 F.3d at 1363."
-“ generating, by the computing system, a shortcut to the action interface using the resource locator” is WURC as evidenced by the court cases cited in MPEP 2106.05(d)(II) by at least "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, ... buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" and "iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, ... Of P Techs., 788 F.3d at 1363."
-“ outputting, by the computing system on a user interface, the at least one search result and the shortcut and outputting, by the computing system, the trained machine-learned action prediction model to update a browser application of a second set of client devices, the browser application configured to use the trained machine-learned model to determine a resource locator of an action interface of a web resource associated with at least one search result presented within the browser” is WURC as evidenced by the court cases cited in MPEP 2106.05(d)(II) by at least "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, ... buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" and "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
The dependent claims 2-3, 6-8, 10-11, 14-15, 17-21, 23 and 28-30 have been fully considered as well, however, similar to the findings for claims above, these claims are similarly directed to the above-mentioned groupings of abstract ideas set forth in the 2019 PEG, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims. The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 7-8, 10-11, 14-15, 17-21 and 23 are rejected under 35 U.S.C. 103(a) as being unpatentable over Burges et al (US 2011/0161260 A1) in view of Rohde (US 2023/0359612 A1).
As per claim 1, Burges teaches a computer-implemented method, the method comprising: transmitting, by a computing system and to a search system, a search query for retrieving search results indicating web resources related to the search query and receiving, by the computing system and from the search system, the search results ([0017], e.g., wherein discloses when a user with a browser submits a query to a search engine, the search engine uses a ranking system to find matching web pages and rank the results, the results may be in the form of an HTML web page with links (URLs) to the matching web pages); determining, by the computing system and using a machine-learned action prediction model, based on context data associated with the search query, a resource locator of an action interface of a web resource associated with at least one search result, wherein the machine- learned action prediction model was trained using a training set of action sequences, a respective training action sequence describing an order in which web resources were accessed (see abstract and [0022]-[0023], e.g., wherein discloses Clickcounts of respective training URLs may indicate a number of times that corresponding training URLs were clicked in search engine results, a machine learning algorithm implemented on a computer computes a trained model that is then stored, the clickcounts and respective URLs are passed to the machine learning algorithm to train the model to predict probabilities based on feature vectors of URLs, the feature may take various forms a click rate or may reflect user behavior toward the URL and in particular when the URL was included in a search engine result set or clicked within a search result set);
Burges does not explicitly teach generating, by the computing system, a shortcut to the action interface using the resource locator and outputting, by the computing system on a user interface, the at least one search result and the shortcut.
However, Rohde teaches generating, by the computing system, a shortcut to the action interface using the resource locator and outputting, by the computing system on a user interface, the at least one search result and the shortcut (see page 13 and description of claim 21, e.g., wherein discloses creating a shortcut to include at least a uniform resource locator (URL) and a category for a user-selected one of the one or more suggested results; and displaying the shortcut in the user interface as a user-selectable item for accessing the one or more sites via the URL).
Thus, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to apply the teachings of Rohde with the teachings of Burges in order to efficiently enabling a system to create and display the shortcut in the user interface as a user-selectable item for accessing the one or more sites via the URL.
As per claim 2, generating, by the computing system, an input to the machine-learned action prediction model, wherein the input comprises a sequence of web resources accessed prior to transmission of the search query ([0008], [0017], Burges).
As per claim 3, wherein the context data comprises at least one data type selected from the following list: user account data, location data, or sensor data ([0008], [0017], Burges).
As per claim 7, comprising: inputting, by the computing system, the context data into the machine-learned action prediction model, wherein the context data comprises a set of one or more runtime actions; and outputting, by the computing system, a predicted action in a sequence of actions comprising the one or more runtime actions ([0022], Burges).
As per claim 8, wherein the machine-learned action prediction model outputs a representation of the resource locator ([0017]-[0023], Burges).
As per claim 10, wherein the context data comprises data representing contents of the web resource ([0021]-[0023], Burges).
As per claim 11, wherein the context data comprises data representing a sitemap associated with the web resource ([0025]-[0026], Burges).
As per claim 14, comprising: inputting, by the computing system and to the machine-learned action prediction model, the data representing resource locators for a the plurality of web resources that share a the second-level domain with the web resource; and selecting, by the computing system and using the machine-learned action prediction model, the resource locator from the data representing resource locators for the plurality of web resources that share the second-level domain with the web resource ([0008]-[0023], Burges).
As per claim 15, comprising: obtaining, by the computing system, a verified set of action sequences; and determining, by the computing system and using the machine-learned action prediction model, the resource locator by: determining a relevant action sequence of the verified set of action sequences, the relevant action sequence related to the web resource associated with the at least one search result and returning the resource locator associated with the action interface ([0008]-[0023], Burges).
As per claim 17, wherein the machine-learned action prediction model is configured to process, as an input, a sequence of tokens representing one or more resource locators ([0016]-[0017], Burges).
As per claim 18, wherein the machine-learned action prediction model is configured to output a sequence of tokens representing one or more resource locators ([0016]-[0023], Burges).
As per claim 19, wherein a token of the sequence of tokens represents one or more subportions of a resource locator ([0017]-[0023], Burges).
As per claim 20, wherein a token of the sequence of tokens represents an entire resource locator ([0017]-[0023], Burges).
As per claim 21, performed by a browser application ([0017], Burges).
As per claim 23, comprising: caching, by the computing system, action sequences performed by the browser; uploading, by the computing system and to a training system, the cached action sequences; and receiving, by the computing system, an updated machine-learned action prediction model trained using the cached action sequences ([0017]-[0023], Burges).
Claim 6 is rejected under 35 U.S.C. 103(a) as being unpatentable over Burges et al (US 2011/0161260 A1) in view of Rohde (US 2023/0359612 A1) further in view of Guha et al (US 8868539 B2).
As per claim 6, Burges teaches generating, by the computing system and using the machine-learned action prediction model, a plurality of scores associated with a plurality of resource locators ([0005], [0019]);
Burges and Rohde do not explicitly teach determining, by the computing system and based on the score, to generate a plurality of shortcuts using a top-ranked set of the plurality of resource locators and outputting, by the computing system, the plurality of shortcuts with the at least one search result.
However, Guha teaches determining, by the computing system and based on the score, to generate a plurality of shortcuts using a top-ranked set of the plurality of resource locators and outputting, by the computing system, the plurality of shortcuts with the at least one search result (see Fig.5A, e.g., a set of shortcuts selected from the search context deemed most relevant (e.g., having the highest relevance score), the top two (or another relatively small number) most relevant search contexts, etc., are sent to the client, the shortcuts are interactively displayed with the client to users).
Thus, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to apply the teachings of Guha with the teachings of Burges and Rohde in order to efficiently enabling a system for displaying shortcuts to the user based on the on the relevance scores for the search contexts and the association between the shortcuts and search contexts.
Claims 27-30 are rejected under 35 U.S.C. 103(a) as being unpatentable over Shalaby et al (US 2023/0195819 A1) in view of Benbrahim et al (US 2019/0178657 A1) further in view of Burges et al (US 2011/0161260 A1).
As per claim 27, Shalaby teaches a computer-implemented method, comprising: obtaining, by a computing system, a training set of action sequences received from a browser application of a first set of client devices ([0018]-[0020], e.g., wherein discloses receiving a sequence of user actions, the sequence of user actions may be a user’s interactions with the interface with which the training data used at block 202 is associated, for example, documents accessible through a given interface, such as a website or mobile application and a server may provide a website, data for a mobile application, or other interface through which the users of the user computing devices 116); training, by the computing system, a machine-learned action prediction model to predict, for a respective training action sequence, an action based on one or more preceding actions in the respective training action sequence ([0021]-[0023], e.g., wherein discloses each new user action may be input to the trained model, such that the trained model is predicting a next user action in response to each new user action, based on the sequence of prior user actions);
Shalaby does not explicitly teach outputting, by the computing system, the trained machine-learned action prediction model to update a browser application of a second set of client devices.
However, Benbrahim teaches outputting, by the computing system, the trained machine-learned action prediction model to update a browser application of a second set of client devices ([0110]-[0118], e.g., wherein discloses a computer application implement algorithms which capture at least one user's content browsing actions and broadcast those actions to at least one other user in the group and wherein an algorithm implemented by a computer application predict user actions through one or more machine learning techniques and broadcast/update user actions predicted by the one or more machine learning techniques to one or more other users).
Thus, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to apply the teachings of Benbrahim with the teachings of Shalaby in order to efficiently enabling a system to identify internet browsing data of a user and distributing to one or more other users.
Shalaby and Benbrahim do not explicitly teach the browser application configured to use the trained machine-learned model to determine a resource locator of an action interface of a web resource associated with at least one search result presented within the browser.
However, Burges teaches the browser application configured to use the trained machine-learned model to determine a resource locator of an action interface of a web resource associated with at least one search result presented within the browser (see abstract and [0022]-[0023], e.g., wherein discloses Clickcounts of respective training URLs may indicate a number of times that corresponding training URLs were clicked in search engine results, a machine learning algorithm implemented on a computer computes a trained model that is then stored, the clickcounts and respective URLs are passed to the machine learning algorithm to train the model to predict probabilities based on feature vectors of URLs, the feature may take various forms a click rate or may reflect user behavior toward the URL and in particular when the URL was included in a search engine result set or clicked within a search result set and [0017], E.G., a user with a browser submits a query to a search engine, the search engine uses a ranking system to find matching web pages and rank the results and search results are presented to the users 104 ).
Thus, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to apply the teachings of Burges with the teachings of Shalaby and Benbrahim in order to enabling a system to train a machine learning based model which is used to predict which web pages are likely to be searched in efficient manner.
As per claim 28, wherein a respective training action sequence of the training set comprises: a first resource locator associated with a first web resource accessed at a first time and a second resource locator associated with a second web resource accessed at a second time subsequent to the first time ([0017]-[0028], Burges).
As per claim 29, comprising: generating, by the computing system, a verified set of action sequences and training, by the computing system, the machine-learned action prediction model on the verified set of action sequences ([0020], Shalaby).
As per claim 30, verifying, by the computing system and based on a measure of recurrence of the action sequence in the training set, an action sequence using a machine-learned verification model ([0021], Shalaby).
It is noted that any citation [[s]] to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any wav. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. [[See, MPEP 2123]].
Citation of Pertinent Prior Arts
The prior art made of record and not relied upon in form PTO-892, if any, is considered pertinent to applicant's disclosure.
Conclusion
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/Mohammad A Sana/Primary Examiner, Art Unit 2166